Recovering time-varying networks of dependencies in social and biological studies |
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| Authors: | Amr Ahmed and Eric P. Xing |
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| Affiliation: | Language Technology Institute and Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213 |
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| Abstract: | A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. Although there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this article, we present a machine learning method called TESLA, which builds on a temporally smoothed l1-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently by using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over >4,000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency. |
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| Keywords: | evolving network social network gene network lasso Markov random field |
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